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Memory Stocks Are Down for the Wrong Reasons
Google's TurboQuant reduces memory use by six times for AI models, pressuring chip stocks globally despite analysts predicting long-term market growth.
- Alphabet's Google unveiled TurboQuant on Tuesday, a compression algorithm reducing memory demands for large language models by six times through extreme compression of key-value caches, a crucial component of AI model serving.
- The efficiency breakthrough targets a major goal of leading AI labs amid ongoing infrastructure expansion, though investors fear it could dampen demand for memory chips critical to training large language models from Google, OpenAI and Anthropic.
- Memory stocks plunged globally Thursday as investors panicked over reduced chip demand, with SK Hynix and Samsung falling 6% and nearly 5% respectively in South Korea, while Japanese Kioxia, U.S.-based Sandisk and Micron, and Chinese GigaDevice Semiconductor and Montage Technology also declined.
- Analysts attributed the selloff partly to profit-taking in a cyclical sector, with Ben Barringer of Quilter Cheviot noting memory stocks gained nearly 200% last year, while Morgan Stanley's Shawn Kim suggested TurboQuant could reduce inference costs by increasing chip throughput.
- Ray Wang of SemiAnalysis argued the research won't reduce overall chip demand, citing the Jevons Paradox where efficiency gains increase total demand as services become cheaper, a view supported by Cloudflare CEO Matthew Prince, who called the breakthrough comparable to DeepSeek's efficiency gains.
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Coverage Details
Total News Sources19
Leaning Left3Leaning Right1Center5Last UpdatedBias Distribution56% Center
Bias Distribution
- 56% of the sources are Center
56% Center
L 33%
C 56%
11%
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